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地理科学与资源研究... [51]
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期刊论文 [49]
SCI/SSCI论文 [2]
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2021 [12]
2020 [16]
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专题:地理科学与资源研究所
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Spatiotemporal Monitoring of Soil CO2 Efflux in a Subtropical Forest during the Dry Season Based on Field Observations and Remote Sensing Imagery
期刊论文
REMOTE SENSING, 2021, 卷号: 13, 期号: 17, 页码: 23
作者:
Chen, Tao
;
Xu, Zhenwu
;
Tang, Guoping
;
Chen, Xiaohua
;
Fang, Hong
收藏
  |  
浏览/下载:66/0
  |  
提交时间:2021/11/05
forest soil CO2 flux
satellite remote sensing data
random forest algorithm
subtropical forest ecosystem
Improving terrestrial evapotranspiration estimation across China during 2000-2018 with machine learning methods
期刊论文
JOURNAL OF HYDROLOGY, 2021, 卷号: 600, 页码: 18
作者:
Yin, Lichang
;
Tao, Fulu
;
Chen, Yi
;
Liu, Fengshan
;
Hu, Jian
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  |  
浏览/下载:55/0
  |  
提交时间:2021/11/05
Evapotranspiration
Machine learning
process-based ET
ET integration
China
Gaussian process regression
High-accuracy continuous mapping of surface water dynamics using automatic update of training samples and temporal consistency modification based on Google Earth Engine: A case study from Huizhou, China
期刊论文
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2021, 卷号: 179, 页码: 66-80
作者:
Li, Kewei
;
Xu, Erqi
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  |  
浏览/下载:36/0
  |  
提交时间:2021/11/05
Surface water
Time series
Auto-updating of training samples
Temporal consistency detection
Change monitoring
Intra-annual information
Assessment of Empirical Algorithms for Shallow Water Bathymetry Using Multi-Spectral Imagery of Pearl River Delta Coast, China
期刊论文
REMOTE SENSING, 2021, 卷号: 13, 期号: 16, 页码: 17
作者:
Wei, Chunzhu
;
Zhao, Qianying
;
Lu, Yang
;
Fu, Dongjie
收藏
  |  
浏览/下载:39/0
  |  
提交时间:2021/11/05
bathymetry
Landsat 8
Sentinel-2
Google Earth Engine
retrieval algorithm
Cloud Detection Algorithm for Multi-Satellite Remote Sensing Imagery Based on a Spectral Library and 1D Convolutional Neural Network
期刊论文
REMOTE SENSING, 2021, 卷号: 13, 期号: 16, 页码: 20
作者:
Ma, Nan
;
Sun, Lin
;
Zhou, Chenghu
;
He, Yawen
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  |  
浏览/下载:24/0
  |  
提交时间:2021/11/05
ASTER spectral library
hyperspectral data
1D convolutional neural network
cloud detection
data simulation
multi-satellite remote sensing images
Optimization of multi-source UAV RS agro-monitoring schemes designed for field-scale crop phenotyping
期刊论文
PRECISION AGRICULTURE, 2021, 页码: 35
作者:
Zhu, Wanxue
;
Sun, Zhigang
;
Huang, Yaohuan
;
Yang, Ting
;
Li, Jing
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  |  
浏览/下载:122/0
  |  
提交时间:2021/06/10
Unmanned aerial vehicle (UAV)
Multispectral
Hyperspectral
Thermal
LiDAR
Phenotyping
Mapping Forest Types in China with 10 m Resolution Based on Spectral-Spatial-Temporal Features
期刊论文
REMOTE SENSING, 2021, 卷号: 13, 期号: 5, 页码: 21
作者:
Cheng, Kai
;
Wang, Juanle
;
Yan, Xinrong
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浏览/下载:45/0
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提交时间:2021/06/10
forest type
spectral–
spatial–
temporal features
image composition
random forest algorithm
China
Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data
期刊论文
REMOTE SENSING, 2021, 卷号: 13, 期号: 6, 页码: 33
作者:
Zhong, Yang
;
Lin, Aiwen
;
Xiao, Chiwei
;
Zhou, Zhigao
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  |  
浏览/下载:22/0
  |  
提交时间:2021/07/09
YREB
three urban agglomerations
DMSP
OLS night light data
EPC
spatiotemporal dynamics
Geographically weighted regression (GWR) model
random forest algorithm
Quantifying influences of anthropogenic-natural factors on ecological land evolution in mega-urban agglomeration: A case study of Guangdong-Hong Kong-Macao greater Bay area
期刊论文
JOURNAL OF CLEANER PRODUCTION, 2021, 卷号: 283, 页码: 10
作者:
Feng, Rundong
;
Wang, Fuyuan
;
Wang, Kaiyong
;
Xu, Shaojie
收藏
  |  
浏览/下载:39/0
  |  
提交时间:2021/03/15
Ecological land
Guangdong-Hong Kong-Macao Greater bay area
Spatial-temporal evolution
Anthropogenic-natural factors
Interaction effect
Geographical detector
Modern pollen assemblages of the surface lake sediments from the steppe and desert zones of the Tibetan Plateau
期刊论文
SCIENCE CHINA-EARTH SCIENCES, 2021, 页码: 15
作者:
Qin, Feng
收藏
  |  
浏览/下载:16/0
  |  
提交时间:2021/03/15
Modern pollen assemblage
Tibetan Plateau
Surface lake sediment
Pollen-climate relationship
Pollen-based vegetation classification model
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